Sr. Systems Engineer

Job highlights

• Bachelor s / Master s degree preferably in Engineering,Computer Science,Statistics,Data Science,or related disciplines
• Candidates from any other discipline with strong experience in AI / ML,data analytics,and cloud
• Leverage emerging AI / ML technologies (Generative AI,NLP,Computer Vision) to drive innovation and business optimization. . Qualification Required

STL Digital is a global IT Services and Consulting company that enables enterprises to experience the future of digital transformation. We provide end to end services across product engineering, software, cloud, data and analytics, enterprise application services, and cyber-security. Purpose - We Engineer Experiences that create value. We blend the agility of a startup with the stability of an established enterprise. Were passionate about innovation. Our culture is built on 4 core values: 1. Respect for Individuals: We value every team members unique perspective and contributions. 2. Hunger to Learn: We encourage continuous growth and development. 3. Promises Delivered: We are committed to delivering on our commitments. 4. Keep it Simple: We strive for clarity and efficiency in everything we do. Were looking for talented individuals to join us on this exciting journey, working with our 25+ Global Customers. Lets build the future of tech together. Job Title : Data Scientist Work Location : Udaipur Experience : 3-5 Yrs. Job Mode : Work from office Responsibilities: Role Overview We are seeking a Data Scientist to lead AI/ML initiatives and deliver end-to-end analytics solutions on Microsoft Azure. The role spans data engineering, model development, deployment, monitoring, and governance combining Python, SQL, and machine learning expertise with practical cloud deployment skills

to solve business challenges at scale. This position offers exposure to predictive & prescriptive analytics,

• computer vision, and Generative AI, along with hands-on experience in MLOps and cloud orchestration,

• enabling candidates to drive digital transformation while accelerating their professional growth at the intersection of data, cloud, and business impact.

Key Responsibilities

1. Data Acquisition & Understanding

Collaborate with stakeholders to identify business problems suitable for AI/ML solutions.

Gather structured and unstructured data from multiple sources (PI historian, SQL databases, APIs, IoT

systems, cloud storage).

Perform Exploratory Data Analysis (EDA) to understand data distribution, anomalies, and patterns. 2. Data Cleaning & Feature Engineering Preprocess raw data to handle missing values, outliers, and inconsistencies. Perform feature extraction, transformation, and selection to enhance model performance. Apply dimensionality reduction and encoding techniques where necessary. 3. Model Development & Evaluation Build predictive and prescriptive models using supervised, unsupervised, and reinforcement learning techniques. Implement regression, classification, clustering, time-series forecasting, and deep learning algorithms. Evaluate models using appropriate metrics (accuracy, RMSE, R2-score, precision, recall, confusion matrix) Perform hyperparameter tuning and model optimization. 4. Deployment & Cloud Management Deploy models on MS Azure, ensuring scalability, availability, and security. Maintain and monitor performance of deployed models, retraining as needed. Optimize cloud resource usage and manage pipelines using Azure Machine Learning and related Services. 5. Reporting & Visualization Communicate model insights and recommendations to business stakeholders. Develop dashboards and reports to track model performance and business impact. Ensure reproducibility, documentation, and version control of all models and code. 6. Administration & Governance Ensure proper administration, housekeeping, and lifecycle management of deployed AI/ML models and Azure resources. Support version control, access management, and cost optimization of ML solutions in the cloud with IT Team.

Establish monitoring practices to ensure compliance, security, and sustainability of digital assets.

Leverage emerging AI/ML technologies (Generative AI, NLP, Computer Vision) to drive innovation and business optimization.

Qualification Required

Bachelor s / Master s degree preferably in Engineering, Computer Science, Statistics, Data Science, or related disciplines.

Candidates from any other discipline with strong experience in AI/ML, data analytics, and cloud

deployment (Python + Azure) are encouraged to apply.

Additional certifications such as Microsoft Certified: Azure Data Scientist Associate or Azure AI Engineer Associate will be an added advantage.

Technical Skills & Tools

Programming Languages: Python, SQL Tools & Platforms: Jupyter Notebook (Anaconda), MS Azure, SSMS, Power BI (Optional) Python Libraries & Frameworks: Data Processing & Visualization: pandas, numpy, matplotlib, seaborn, plotly Supervised Learning: scikit-learn, XGBoost Unsupervised Learning: scikit-learn, hdbscan, umap-learn Deep Learning / Computer Vision: TensorFlow, Keras, OpenCV, Detectron2 / YOLO Natural Language Processing / Generative AI (Optional): transformers (GPT, LLaMA), spaCy Algorithms / Techniques: Supervised Learning: Regression (Multi Linear Regression, Logistic Regression), Tree-based Models (Decision Trees, Random Forest), Gradient Boosting (XGBoost) Unsupervised Learning: Clustering (K-Means, Hierarchical Clustering), Dimensionality Reduction (Principal Component Analysis - PCA) Deep Learning: Convolutional Neural Networks - CNNs (for image processing & computer vision), Recurrent Neural Networks - RNNs (LSTMs for sequential data & time series), Autoencoders (for anomaly detection, dimensionality reduction) Generative AI / NLP (Optional): Fundamentals of Transformer Models and Large Language Models (GPT, LLaMA, Claude, Gemini) Cloud & Deployment Skills (MS Azure): Azure Machine Learning (AML): End-to-end ML lifecycle (training, deployment, MLOps). Azure Blob Storage / Data Lake Storage: Raw data storage, feature store, model artifacts. Azure Functions: Serverless compute for lightweight inference or automation tasks. Azure App Service / Azure API Management: Hosting ML models Azure Monitor & Application Insights: Tracking model performance, drift detection, logs, s. Azure Databricks (Optional): Advanced data engineering, large-scale ML model training, Spark-based analytics. Azure Data Factory (Optional): Data ingestion, ETL pipelines, orchestration. Azure Cognitive Services (Optional): Pre-built AI for computer vision, NLP, speech